POTW | City of Greenwood

Straddling Saint Albans Bay along the southeastern shore of Lake Minnetonka and sandwiched between Excelsior and Deephaven is the City of Greenwood. As with most communities boasting a healthy stretch of navigable waterfront—regardless of urban or suburban—Greenwood homes are not only in high demand and short supply, but they also tend to command a price premium compared to similar cities, even when adjusting for square footage.

The median sales price for Greenwood has reached a record high of $1,050,000. That means half the homes in the city sold for less than that, while the other half sold for more. Homes in this area tend to be larger than the metro as a whole, and so it’s useful to control for home size. In that spirit, the median price per square foot is $303 over the last 12 months.

And now for a lesson on small sample size. There’s not a ton of actionable knowledge we can take away from the above chart. And it could frankly confuse prospective home buyers or sellers. This is the monthly data.

But this is the rolling-12 month data, where each data point represents the preceding 12 months worth of activity. The series is still a bit volatile or “noisy,” but this version actually provides some interesting morsels we can take away. Specifically, over the last 12 months, the $1,000,000 – $1,999,999 range has seen both the highest volume and strongest year-over-year gain in listing activity. That would be useful and actionable knowledge for both buyers and sellers in that price range.

Taking a look at absorption rates by price range, we can immediately see that the $499,999 and under range has the tightest balance between supply and demand. In other words, homes listed in that price range are very likely to be absorbed quickly. The range with the slowest absorption rate is $1,000,000 – $1,999,999, tighter than even the $2,000,000 and up range.

Keep in mind this is based on a very limited sample size. When working in smaller areas with limited activity, it’s useful to either expand your geography using a custom area or at least utilize the rolling-12 month time iteration, because it will help to optimize the sample size to provide more meaningful takeaways.